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Zuva, Keneilwe, Zuva, Tranos.  2017.  Diversity and Serendipity in Recommender Systems. Proceedings of the International Conference on Big Data and Internet of Thing. :120–124.

The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.

Zubov, Ilya G., Lysenko, Nikolai V., Labkov, Gleb M..  2019.  Detection of the Information Hidden in Image by Convolutional Neural Networks. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :393–394.

This article shows the possibility of detection of the hidden information in images. This is the approach to steganalysis than the basic data about the image and the information about the hiding method of the information are unknown. The architecture of the convolutional neural network makes it possible to detect small changes in the image with high probability.

Zou, Zhenwan, Chen, Jia, Hou, Yingsa, Song, Panpan, He, Ling, Yang, Huiting, Wang, Bin.  2019.  Design and Implementation of a New Intelligent Substation Network Security Defense System. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 1:2709–2713.
In order to enhance the network security protection level of intelligent substation, this paper puts forward a model of intelligent substation network security defense system through the analysis of intelligent substation network security risk and protection demand, and using example proved the feasibility and effectiveness of the defense system. It is intelligent substation network security protection provides a new solution.
Zondo, S., Ogudo, K., Umenne, P..  2020.  Design of a Smart Home System Using Bluetooth Protocol. 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). :1—5.
Home automation is an intelligent, functional as a unit system that facilitates home processes without unnecessarily complicating the user's life. Devices can be connected, which in turn connect and talk through a centralized control unit, which are accessible via mobile phones. These devices include lights, appliances, security systems, alarms and many other sensors and devices. This paper presents the design and implementation of a Bluetooth based smart home automation system which uses a Peripheral interface controller (PIC) microcontroller (16F1937) as the main processer and the appliances are connected to the peripheral ports of the microcontroller via relays. The circuit in the project was designed in Diptrace software. The PCB layout design was completed. The fully functional smart home prototype was built and demonstrated to functional.
Ziegler, D., Rauter, M., Stromberger, C., Teufl, P., Hein, D..  2014.  Do you think your passwords are secure? Privacy and Security in Mobile Systems (PRISMS), 2014 International Conference on. :1-8.

Many systems rely on passwords for authentication. Due to numerous accounts for different services, users have to choose and remember a significant number of passwords. Password-Manager applications address this issue by storing the user's passwords. They are especially useful on mobile devices, because of the ubiquitous access to the account passwords. Password-Managers often use key derivation functions to convert a master password into a cryptographic key suitable for encrypting the list of passwords, thus protecting the passwords against unauthorized, off-line access. Therefore, design and implementation flaws in the key derivation function impact password security significantly. Design and implementation problems in the key derivation function can render the encryption on the password list useless, by for example allowing efficient bruteforce attacks, or - even worse - direct decryption of the stored passwords. In this paper, we analyze the key derivation functions of popular Android Password-Managers with often startling results. With this analysis, we want to raise the awareness of developers of security critical apps for security, and provide an overview about the current state of implementation security of security-critical applications.

Zhu, Xue, Sun, Yuqing.  2016.  Differential Privacy for Collaborative Filtering Recommender Algorithm. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics. :9–16.

Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history, he/she can infer more information about this user. This brings great threats to user privacy. Some methods adopt differential privacy algorithms in collaborative filtering by adding noises to a rating matrix. Although they provide theoretically private results, the influence on recommendation accuracy are not discussed. In this paper, we solve the privacy problem in recommender system in a different way by applying the differential privacy method into the procedure of recommendation. We design two differentially private recommender algorithms with sampling, named Differentially Private Item Based Recommendation with sampling (DP-IR for short) and Differentially Private User Based Recommendation with sampling(DP-UR for short). Both algorithms are based on the exponential mechanism with a carefully designed quality function. Theoretical analyses on privacy of these algorithms are presented. We also investigate the accuracy of the proposed method and give theoretical results. Experiments are performed on real datasets to verify our methods.

Zhu, Xiaofeng, Huang, Liang, Wang, Ziqian.  2019.  Dynamic range analysis of one-bit compressive sampling with time-varying thresholds. The Journal of Engineering. 2019:6608–6611.
From the point of view of statistical signal processing, the dynamic range for one-bit quantisers with time-varying thresholds is studied. Maximum tolerable amplitudes, minimum detectable amplitudes and dynamic ranges of this one-bit sampling approach and uniform quantisers, such as N-bits analogue-to-digital converters (ADCs), are derived and simulated. The results reveal that like conventional ADCs, the dynamic ranges of one-bit sampling approach are linearly proportional to the Gaussian noise standard deviations, while one-bit sampling's dynamic ranges are lower than N-bits ADC under the same noise levels.
Zhu, K., Wu, B., Wang, B..  2020.  Deepfake Detection with Clustering-based Embedding Regularization. 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). :257—264.
In recent months, AI-synthesized face swapping videos referred to as deepfake have become an emerging problem. False video is becoming more and more difficult to distinguish, which brings a series of challenges to social security. Some scholars are devoted to studying how to improve the detection accuracy of deepfake video. At the same time, in order to conduct better research, some datasets for deepfake detection are made. Companies such as Google and Facebook have also spent huge sums of money to produce datasets for deepfake video detection, as well as holding deepfake detection competitions. The continuous advancement of video tampering technology and the improvement of video quality have also brought great challenges to deepfake detection. Some scholars have achieved certain results on existing datasets, while the results on some high-quality datasets are not as good as expected. In this paper, we propose new method with clustering-based embedding regularization for deepfake detection. We use open source algorithms to generate videos which can simulate distinctive artifacts in the deepfake videos. To improve the local smoothness of the representation space, we integrate a clustering-based embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. We evaluate our method on three latest deepfake datasets. Experimental results demonstrate the effectiveness of our method.
Zhu, Jun, Chu, Bill, Lipford, Heather.  2016.  Detecting Privilege Escalation Attacks Through Instrumenting Web Application Source Code. Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies. :73–80.

Privilege Escalation is a common and serious type of security attack. Although experience shows that many applications are vulnerable to such attacks, attackers rarely succeed upon first trial. Their initial probing attempts often fail before a successful breach of access control is achieved. This paper presents an approach to automatically instrument application source code to report events of failed access attempts that may indicate privilege escalation attacks to a run time application protection mechanism. The focus of this paper is primarily on the problem of instrumenting web application source code to detect access control attack events. We evaluated false positives and negatives of our approach using two open source web applications.

Zhou, Guorui, Zhu, Xiaoqiang, Song, Chenru, Fan, Ying, Zhu, Han, Ma, Xiao, Yan, Yanghui, Jin, Junqi, Li, Han, Gai, Kun.  2018.  Deep Interest Network for Click-Through Rate Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :1059-1068.

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.

Zhongming Jin, Cheng Li, Yue Lin, Deng Cai.  2014.  Density Sensitive Hashing. Cybernetics, IEEE Transactions on. 44:1362-1371.

Nearest neighbor search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, for example, locality sensitive hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbor search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called density sensitive hashing (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.

Zhongchao, W., Ligang, H., Baojun, T., Wensi, W., Jinhui, W..  2017.  Design and Verification of a Novel IoT Node Protocol. 2017 13th IEEE International Conference on Electronic Measurement Instruments (ICEMI). :201–205.

The IoT node works mostly in a specific scenario, and executes the fixed program. In order to make it suitable for more scenarios, this paper introduces a kind of the IoT node, which can change program at any time. And this node has intelligent and dynamic reconfigurable features. Then, a transport protocol is proposed. It enables this node to work in different scenarios and perform corresponding program. Finally, we use Verilog to design and FPGA to verify. The result shows that this protocol is feasible. It also offers a novel way of the IoT.

Zhong, Xiaoxiong, Lu, Renhao, Li, Li, Wang, Xinghan, Zheng, Yanbin.  2019.  DSOR: A Traffic-Differentiated Secure Opportunistic Routing with Game Theoretic Approach in MANETs. 2019 IEEE Symposium on Computers and Communications (ISCC). :1–6.

Recently, the increase of different services makes the design of routing protocols more difficult in mobile ad hoc networks (MANETs), e.g., how to guarantee the QoS of different types of traffics flows in MANETs with resource constrained and malicious nodes. opportunistic routing (OR) can make full use of the broadcast characteristics of wireless channels to improve the performance of MANETs. In this paper, we propose a traffic-differentiated secure opportunistic routing from a game theoretic perspective, DSOR. In the proposed scheme, we use a novel method to calculate trust value, considering node's forwarding capability and the status of different types of flows. According to the resource status of the network, we propose a service price and resource price for the auction model, which is used to select optimal candidate forwarding sets. At the same time, the optimal bid price has been proved and a novel flow priority decision for transmission is presented, which is based on waiting time and requested time. The simulation results show that the network lifetime, packet delivery rate and delay of the DSOR are better than existing works.

Zhenqi Huang, University of Illinois at Urbana-Champaign, Yu Wang, University of Illinois at Urbana-Champaign.  2016.  Differential Privacy, Entropy and Security in Distributed Control of Cyber Physical Systems.

The concept of differential privacy stems from the study of private query of datasets. In this work, we apply this concept to discrete-time, linear distributed control systems in which agents need to maintain privacy of certain preferences, while sharing information for better system-level performance. The system has N agents operating in a shared environment that couples their dynamics. We show that for stable systems the performance grows as O(T3/Nε2), where T is the time horizon and ε is the differential privacy parameter. Next, we study lower-bounds in terms of the Shannon entropy of the minimal mean square estimate of the system’s private initial state from noisy communications between an agent and the server. We show that for any of noise-adding differentially private mechanism, then the Shannon entropy is at least nN(1−ln(ε/2)), where n is the dimension of the system, and t he lower bound is achieved by a Laplace-noise-adding mechanism. Finally, we study the problem of keeping the objective functions of individual agents differentially private in the context of cloud-based distributed optimization. The result shows a trade-off between the privacy of objective functions and the performance of the distributed optimization algorithm with noise.

Presented at the Joint Trust and Security/Science of Security Seminar, April 26, 2016.

Zhenqi Huang, University of Illinois at Urbana-Champaign, Sayan Mitra, University of Illinois at Urbana-Champaign, Geir Dullerud, University of Illinois at Urbana-Champaign.  2012.  Differentially Private Iterative Synchronous Consensus. Workshop on Privacy in the Electronic Society (WPES 2012).

The iterative consensus problem requires a set of processes or agents with different initial values, to interact and update their states to eventually converge to a common value. Pro- tocols solving iterative consensus serve as building blocks in a variety of systems where distributed coordination is re- quired for load balancing, data aggregation, sensor fusion, filtering, and synchronization. In this paper, we introduce the private iterative consensus problem where agents are re- quired to converge while protecting the privacy of their ini- tial values from honest but curious adversaries. Protecting the initial states, in many applications, suffice to protect all subsequent states of the individual participants.

We adapt the notion of differential privacy in this setting of iterative computation. Next, we present (i) a server-based and (ii) a completely distributed randomized mechanism for solving differentially private iterative consensus with adver- saries who can observe the messages as well as the internal states of the server and a subset of the clients. Our analysis establishes the tradeoff between privacy and the accuracy.

Zhenqi Huang, University of Illinois at Urbana-Champaign, Sayan Mitra, University of Illinois at Urbana-Champaign, Nitin Vaidya, University of Illinois at Urbana-Champaign.  2015.  Differentially Private Distributed Optimization. IEEE International Conference on Distributed Computing and Networks (ICDCN 2015), .

In distributed optimization and iterative consensus literature, a standard problem is for N agents to minimize a function f over a subset of Rn, where the cost function is expressed as Σ fi . In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private.  The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual’s cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to the optimal value.  Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm.

Zhao, Pu, Liu, Sijia, Chen, Pin-Yu, Hoang, Nghia, Xu, Kaidi, Kailkhura, Bhavya, Lin, Xue.  2019.  On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). :121—130.
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks are still restricted to very specific settings of threat models (e.g., single distortion metric and restrictive assumption on target model's feedback to queries) and/or suffer from prohibitively high query complexity. To push for further advances in this field, we introduce a general framework based on an operator splitting method, the alternating direction method of multipliers (ADMM) to devise efficient, robust black-box attacks that work with various distortion metrics and feedback settings without incurring high query complexity. Due to the black-box nature of the threat model, the proposed ADMM solution framework is integrated with zeroth-order (ZO) optimization and Bayesian optimization (BO), and thus is applicable to the gradient-free regime. This results in two new black-box adversarial attack generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image classification datasets show that our proposed approaches have much lower function query complexities compared to state-of-the-art attack methods, but achieve very competitive attack success rates.
Zhao, Dexin, Ma, Zhen, Zhang, Degan.  2016.  A Distributed and Adaptive Trust Evaluation Algorithm for MANET. Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :47–54.

We propose a distributed and adaptive trust evaluation algorithm (DATEA) to calculate the trust between nodes. First, calculate the communication trust by using the number of data packets between nodes, and predict the trust based on the trend of this value, calculate the comprehensive trust by combining the history trust with the predict value; calculate the energy trust based on the residual energy of nodes; calculate the direct trust by using the communication trust and energy trust. Second, calculate the recommendation trust based on the recommendation reliability and the recommendation familiarity; put forward the adaptively weighting method, and calculate the integrate direct trust by combining the direct trust with recommendation trust. Third, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. Simulation experiments show that the proposed algorithm can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

Zhang, Z., Zhang, Q., Liu, T., Pang, Z., Cui, B., Jin, S., Liu, K..  2020.  Data-driven Stealthy Actuator Attack against Cyber-Physical Systems. 2020 39th Chinese Control Conference (CCC). :4395–4399.
This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.
Zhang, Xuejun, Chen, Qian, Peng, Xiaohui, Jiang, Xinlong.  2019.  Differential Privacy-Based Indoor Localization Privacy Protection in Edge Computing. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :491–496.

With the popularity of smart devices and the widespread use of the Wi-Fi-based indoor localization, edge computing is becoming the mainstream paradigm of processing massive sensing data to acquire indoor localization service. However, these data which were conveyed to train the localization model unintentionally contain some sensitive information of users/devices, and were released without any protection may cause serious privacy leakage. To solve this issue, we propose a lightweight differential privacy-preserving mechanism for the edge computing environment. We extend ε-differential privacy theory to a mature machine learning localization technology to achieve privacy protection while training the localization model. Experimental results on multiple real-world datasets show that, compared with the original localization technology without privacy-preserving, our proposed scheme can achieve high accuracy of indoor localization while providing differential privacy guarantee. Through regulating the value of ε, the data quality loss of our method can be controlled up to 8.9% and the time consumption can be almost negligible. Therefore, our scheme can be efficiently applied in the edge networks and provides some guidance on indoor localization privacy protection in the edge computing.

Zhang, Xin, Cai, Xiaobo, Wang, Chaogang, Han, Ke, Zhang, Shujuan.  2019.  A Dynamic Security Control Architecture for Industrial Cyber-Physical System. 2019 IEEE International Conference on Industrial Internet (ICII). :148—151.

According to the information security requirements of the industrial control system and the technical features of the existing defense measures, a dynamic security control strategy based on trusted computing is proposed. According to the strategy, the Industrial Cyber-Physical System system information security solution is proposed, and the linkage verification mechanism between the internal fire control wall of the industrial control system, the intrusion detection system and the trusted connection server is provided. The information exchange of multiple network security devices is realized, which improves the comprehensive defense capability of the industrial control system, and because the trusted platform module is based on the hardware encryption, storage, and control protection mode, It overcomes the common problem that the traditional repairing and stitching technique based on pure software leads to easy breakage, and achieves the goal of significantly improving the safety of the industrial control system . At the end of the paper, the system analyzes the implementation of the proposed secure industrial control information security system based on the trustworthy calculation.

Zhang, Xiaoxi, Yin, Yong.  2018.  Design of Training Platform for Manned Submersible Vehicle Based on Virtual Reality Technology. Proceedings of the 31st International Conference on Computer Animation and Social Agents. :90-94.
Aiming at the problems of long training time, high cost and high risk existing in the deep working oceanauts, this paper, based on virtual reality technology, designed and developed the simulation system of diving and underwater operation process of Jiaolong which possesses multiple functions and good interactivity. Through the research on the motion model of A-frame swing, use Unity3D engine to develop the interactive simulation of diving and underwater operation process of Jiaolong after the 3D model of Jiaolong and mother ship was built by 3DMax. On the basis of giving full consideration to user experience, the real situation of diving and underwater operation process of Jiaolong was simulated, and the interactive manipulation function was realized.
Zhang, X., Cao, Y., Yang, M., Wu, J., Luo, T., Liu, Y..  2017.  Droidrevealer: Automatically detecting Mysterious Codes in Android applications. 2017 IEEE Conference on Dependable and Secure Computing. :535–536.

The state-of-the-art Android malware often encrypts or encodes malicious code snippets to evade malware detection. In this paper, such undetectable codes are called Mysterious Codes. To make such codes detectable, we design a system called Droidrevealer to automatically identify Mysterious Codes and then decode or decrypt them. The prototype of Droidrevealer is implemented and evaluated with 5,600 malwares. The results show that 257 samples contain the Mysterious Codes and 11,367 items are exposed. Furthermore, several sensitive behaviors hidden in the Mysterious Codes are disclosed by Droidrevealer.

Zhang, X., Li, R., Zhao, W., Wu, R..  2017.  Detection of malicious nodes in NDN VANET for Interest Packet Popple Broadcast Diffusion Attack. 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). :114–118.

As one of the next generation network architectures, Named Data Networking(NDN) which features location-independent addressing and content caching makes it more suitable to be deployed into Vehicular Ad-hoc Network(VANET). However, a new attack pattern is found when NDN and VANET combine. This new attack is Interest Packet Popple Broadcast Diffusion Attack (PBDA). There is no mitigation strategies to mitigate PBDA. In this paper a mitigation strategies called RVMS based on node reputation value (RV) is proposed to detect malicious nodes. The node calculates the neighbor node RV by direct and indirect RV evaluation and uses Markov chain predict the current RV state of the neighbor node according to its historical RV. The RV state is used to decide whether to discard the interest packet. Finally, the effectiveness of the RVMS is verified through modeling and experiment. The experimental results show that the RVMS can mitigate PBDA.

Zhang, Tong, Chen, C. L. Philip, Chen, Long, Xu, Xiangmin, Hu, Bin.  2018.  Design of Highly Nonlinear Substitution Boxes Based on I-Ching Operators. IEEE Transactions on Cybernetics. 48:3349—3358.

This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators-intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.